My current research is on developing highly-expressive but tractable machine learning models and algorithms in order to build more intelligent systems and tools.
Applications include deep learning, probabilistic modeling, computer vision, nonconvex optimization, and protein folding.

Unifying Sum-Product Networks and Submodular Fields.
Abram L. Friesen and Pedro Domingos (2017).
In Proceedings of the 1st Workshop on Principled Approaches to Deep Learning at the International Conference on Machine Learning (PADL at ICML). Sydney, Australia. August, 2017.
(pdf)(supplement)(poster)

The Sum-Product Theorem: A Foundation for Learning Tractable Models.
Abram L. Friesen and Pedro Domingos (2016).
In Proceedings of the 33rd International Conference on Machine Learning (ICML). New York, New York. June, 2016.
(pdf)(supplement)(poster)